Digital panacea or not: Platform Participation

–A critical approach to quantify mass qualitative urban experience from social media platform

Yuanzhao WANG
Supervisor: Carole Voulgaris

Abstract

Understanding and reflecting urban dwellers’ happiness, and perception of urban space and life in urban planning has long been a challenge in China. The Chinese government has progressively brought attention to public participation and views, which had previously been ignored. However, government approaches for fostering public engagement remain scarce, while enthusiasm among individuals to express feelings and comments on government planning is lacking. Over 70% of the Chinese population has access to the internet, and 68% of them utilize social media, which can serve as a platform for gathering public sentiment and feelings. In this context, my research project proposes measuring individual citizens’ emotions and characteristics of the built environment, the proximity to various urban amenities, to explore the relationships between citizens’ sentiments, urban space, and urban activities. This approach can be applied to gather public feelings in the urban planning process through platforms, as a way to quantify the quality of urban life. However, research reveals serious flaws in existing social media data that could merely provide satisfied information for urban planning. The research examines the strengths and weaknesses of social media platforms and proposes a vision for using social media to invite bottom-up citizen participation in urban planning.

Introduction

In China, the early stage of urbanization was subject to the national ambition and development, whose aim at boosting the economic production and consumption. As such, hundreds and thousands of cities and towns were built to serve for this purpose that plays an important role in improving the national economy based on the scarification of natural resource and incline of factory production. However, since the decreasing demand for industrial production and the arise of urban awareness of residents happiness, Chinese government increasingly focus on citizen wellbeing, urban governance and the urban environment.

The new urban policies in China shifted the emphasis of urbanization away from economic development and toward human-centric development, improving residents’ wellbeing and building new ecological smart cities. However, the evaluation of residents’ wellbeing and urban planning process remains top-down and entirely conducted by governments and experts. The qualitative surveys and reports could only cover a small proportion of population, and it becomes even harder and time-consuming as the population in cities and towns reached 848 millions.

With the introduction of social media data and machine learning technologies, new methods for studying urban spatial patterns and residents’ life have emerged. In 2022, there were more than 1.02 billion people have access to mobile internet, while 68% of them frequently use social media platform. The AI-based technology such as sentiment analysis could be used to extract individual feelings from text-based information, as a form of public perception of urban space, contributing to bottom-up engagement and people-oriented urban planning.

In comparison to the traditional top-down approaches to urban research, emerging technologies and smart city concepts becomes a temptation for both government and academia. However, prior to blindly applying urban big data, a thorough validation is required even it seems to be promising. The author proposes a method for gathering public sentiment via social media, investigates its relationship to the built environment, and examine its potential to urban planning strategies. The discussion of the strengths and drawbacks of existing social media data leads to a proposal for an improved application of social platforms for public participation.

Literature Review

Wellbeing as a Measure of Health and the Built environment

Scientists have historically measured well-being using objective indicators (e.g., GDP, health, employment, literacy, poverty) and increasing measured subjective well-being that influences individual life. Modern measures of well-being that account for cognitive evaluations (i.e., evaluative well-being) and reactions to experiences (i.e., experienced well-being) have therefore become the “currency of a life that matters” (Rath et al., 2010). As the concept of well-being develops, the indices including physical health, mental health, air quality and more are increasingly used, implying a strong relationship between health and residents’ well-being (Diener et al., 1999; Lawless & Lucas, 2010). Some studies found that population density may affect well-being on the city level (Florida et al., 2013). Mouratidis (2018) argues that compact urban form with better public transport, accessibility, the mix of land uses, and density positively influences neighborhood well-being. Social and human capital, considered significant drivers of urban well-being, can be affected by safety, educational opportunities, and access to arts and culture (Leyden et al., 2011; Florida et al., 2013). Other aspects of urban infrastructure (such as roads and transportation) impact commute time and connectedness, both of which are related to happiness (Yin et al., 2021; Gim, 2021).

Big data, Social media and Sentiment analysis

Recently, the availability of mobile information, big urban data and machine learning technology has significantly enhanced urban research methods, particularly in terms of the dynamic spatial and temporal relationships between human behaviour and urban research (Wu, Ye, Ren, & Du, 2018). The invitation of various big data could change China’s top-down urban planning process by bringing individual information into the discussion of urban space and quality. With the rise of social media and machine learning, sentiment analysis became a field of study that mines public opinions, measures subjective happiness and relates them to different research areas. The previous studies applied Twitter, Sina Weibo data (Chinese Twitter) and Dazhong dianping (Chinese Yelp) to examine the relationship between urban form, spatial quality, and public comments, such as the relationship between quality of urban parks and travellers’ behaviour and words; urban transportation and commercial facilities (Li et al., 2018; Ta et al., 2020; Yao et al., 2019; Plunz et al., 2019). Some research further studied the difference and distribution of residents’ emotions regarding gender, time-dimension and different urban facilities (Ma et al., 2020; Zhen et al., 2018).

Quantitative urban measure of the built environment

The measurement of the built environment is constructed by a variety body of indices to address different urban issues. Cervero and Kockelman’s developed initial “three Ds” (density, design, and diversity) in 1997 to evaluate the existing urban built environment. Edwing et al. expanded on this concept by adding two Ds (destination accessibility and transportation distance) (Ewing & Cervero, 2001; Ewing et al., 2009). More Ds were added afterwards to reflect the changing built environment, such as Demand management and Demographics (Ewing & Cervero, 2010). Scholars have modified the list of variables based on these quantitative frameworks to comprehensively examine the built environment while addressing various urban issues and topics. Some research used relative entropy to discern compactness from sprawl in the built environment (Tsai, 2005). Others used a multi-metric urban intensity index at a metropolitan scale, which included land use, infrastructure, and landscape variables in addition to density and compactness (Tate et al., 2005). More recent studies, especially in the Chinese context, Rowe et al. (2014) proposed the measurement of urban intensity from variables of compactness, density, diversity, and connectivity, aiming at revealing the resource distribution, transportation efficiency, and social integration in both cities and optimize the urban performance (Rowe et al., 2014). Later, Guan and Rowe (2016) evaluated the spatial structure of small towns in Zhejiang Province using similar urban intensity characteristics, such as density, compactness, diversity, and accessibility.

Limitation

  • First, although the relationships between public sentiment and the proximity to amenities are indicated by a set of regression models, there are no proof to suggest any causation;
  • Second, in this research, author assumed the public sentiment from social media could represent individual real-time happiness, which remains uncertain awaiting a further examination;
  • Third, due to the limited urban variables, the confidence level of regression model is relatively low at 2%, which could be used to study the changes of individual sentiment but is incapable to simulate a convincing results;
  • Forth, the inherent conflict between short-term resident’s feeling from existing social media data and long-term urban planning process is recognized;
  • Fifth, the existing social media data is not specific for the built environment or urban experience but daily expression of everyday life;
  • Sixth, the weibo post could only represent the users who mostly age from 16-40, which are around 30% of the total population who have access to internet.

Reperceive the urban environment by social media posts

Methodology

The research aims to apply sentiment analysis to quantify qualitative public sentiment from text-based information as a representation of individual real-time feeling, and develop a multivariate model to explore the relationship between the individual feelings from social media and the built environment in China. Based on the model, this research compare existing condition and the planned condition of Jiaxing city in China, and test different planning scenarios to explore the possible changes of individual feeling when using social media platform.

Site

The study area is Jiaxing city in China’s Zhejiang province. Jiaxing is a significant city that is part of the Yangtze River Delta city cluster and the Shanghai metropolitan area. It is located in close proximity to the two major cities of Shanghai and Hangzhou. It is a small tourist city with two counties, 44 towns, and 809 administrative villages that has been designated as a national key program.

Data collection and preprocess

For this research, social media data (Weibo posts) was collected from Sina Weibo, which is one of the most used online social platform in China. The collected Weibo posts are restricted with the tag “Jiaxing” in 2018, which limited the posts related to the case city. The untreated data included a large amount of advertisements, which have been removed by identifying certain keywords such as “advertisement”, “cost performance”, etc. The urban amenity data is collected from Gaode Map as spatial POI (points of interest). The regulatory detailed planning (RDP) documents from 2017-2020 issued by the local government in Jiaxing are collected from the local government office.

Sentiment analysis of social media post

To study individual happiness and its response to different built environments, this research collects within one year as a primary resource via the API of Sina Weibo. Individual emotional preference (positive probability of individual post ) would be defined as individual real-time happiness toward the built environment. The Baidu AI platform has allowed researchers to analyze public sentiment via its well-developed emotion dictionaries and its trained machine learning model for decades in the Chinese context. Thus, the sentiment analysis of individual posts will be conducted by accessing a machine learning model from the Baidu AI platform. The return value of individual post contains three parts: sentiment category, positive probability and negative probability. In this research, the positive probability is used to represent the possible level of happiness of individual post.

Proximity to urban amenities

In this research, proximity is defined by the accessibility of urban activities and amenities by walking and by characteristics of pedestrian networks. The amenities dataset is collected as points of interest (POIs) from Gaode Map in China, which is one of the most widely used digital navigation systems in China. The urban networks are extracted from the OpenStreet Map (OSM). Accessibility will be calculated by the R5 package embedded in the R studio.

Correlation between individual sentiment and the built environment

To understand how the urban environment would affect individual sentiment from social media, a series of regression models between sentiment results and the proximity to urban amenities were conducted.To further improve the interpretation of regression model, the regression analysis between sentiment result and the different proximity based on various walking distance were applied. In addition, adding the time, weather and workday variables helps improve the model and further understand which variable might affect individual sentiment other than the built environment.

Regression analysis on the relationship between individual sentiment and the proximity to urban amenities

The regression model shows that proximity to restaurant, busstop, green space, leisure space are positively related to individual sentiment on weibo post; while the proximity to school and small business are negatively related to individual sentiment. In addition, adding the time, weather and workday variables, I found that the Time of the weibo post is statistic significantly related to individual sentiment, while weather and workday variables are not related to individual sentiment.

The threshold tests indicate the best fits of walking distance to different amenities for the regression models: the proximity to restaurant within 15 mins, the proximity to school within 25 mins, the proximity to green space within 25 mins, the proximity to hospital within 25 mins, the proximity to bus stops within 70 mins, the proximity to leisure space within 20 mins, the proximity to small business within 15 mins, the proximity to shopping center within 25 mins.



(Interactive Map)

Digital panacea or not for urban planning

Prediction of sentiment changes based on the regression model

By applying the established regression model, the positive probability of individual post can be simulated by providing a set of proximity to urban amenities which can be calculated by the geo-locations of them. As such, this research provides three different planning for scenarios: the official planning documents, educational city (double the number of school) and tourism city (replace the school with greenspace). Each scenario associates with individual set of urban amenities, simulating different individual sentiment map of Jiaxing. By comparing the scenarios and the exiting condition, we could better understand how the built environment may affect individual feelings in social media.

Scenario1

The planning scenario 1 of Jiaxing is based on the offical planning document issued by local government. Base on the changes of residential and business zoning in Jiaxing, the simulation of new restaurant locations are generated by assuming the same density of restaurant in these zoning. After comparing of the averages of public sentiment between the planned condition and existing condition, it was surprising to find that the simulated future sentiment was less than the existing sentiment result at -0.05382951. It suggests that the future Jiaxing might not improve residents’ happiness in social media.

(Interactive Map)

Scenario2

Scenario 2 intends to simulate an educational Jiaxing city by doubling the number of school spreading out the city. The original schools remind the same locations, while randomly generating same amount of schools spreading out the city. After comparing of the averages of public sentiment between the scenario 2 and existing condition, the simulated sentiment was less than the existing sentiment result at -0.0002921917 due to the negative relationship between school and public sentiment. This finding remains questionable; it is understandable that people might not be happly to stay at school, however, it should play an important role in long-term wellbeing.

(Interactive Map)

Scenario3

Scenario 3 intends to simulate an green Jiaxing city by turning school into green space to optimize individual sentiment from social media. The planning strategy based on the negative correlation between school and public sentiment, while a positive correlation between green space and public sentiment, assuming a happier Jiaxing eliminating all the school.The result shows that the simulated average sentiment was more than the existing sentiment result at 0.005517993, which means it “improves” happiness by 1 percent even it does not make sense as an urban planning strategy.

(Interactive Map)

Platform participation

Strengths and weaknesses of existing social media data

One of the strengths of social media data is its large coverage of population in China. It demonstrates a prospective method for widespread public participation that is distinct from other traditional methods that are time-consuming and involve a small number of representatives. The usage of social media platforms is already embedded in people’s daily lives, which facilitates public participation, particularly for those in China who merely contributed to urban planning process before.

However, it should be noticed that there are critical shortcomings of the existing social media data:

  • the existing social media data from weibo posts express daily life rather than specific individual happiness;
  • the existing social media data from weibo posts are not for urban experience in the built environment;
  • the existing social media data from weibo posts are subject to the relevant social platform so as to the targeted groups of people.

Inherent conflict between short-term feeling and long-term urban planning process

After a careful investigation of weibo posts, we can see that the context mainly represents individual real-time feelings, which implied a shot-term sentiment or comments for minutes even seconds. However, urban planning lasts for years and decades that affects people’s life in long-term. It exists an inherent conflict between them that seems incompatible. The previous section of planning strategy trying to optimize public sentiment demonstrates it clearly that if we blindly design a city to improve short-term feeling, it will not improve long-term well-being (if we replace all the schools and hospitals with green spaces).

It should be noted that this does not negate the importance of short-term feelings for urban planning; rather, it is critical for urban planners to understand how the built environment impacts individual short-term feelings and experiences. Individual short-term feeling can become an indicator for planner to balance short-term happiness and long-term welling when making urban decisions.

A proposed social media platform

Social media platform serves as a new form of infrastructure of urban life, which is embedded in daily experience.The strengths of social media platform can provide a mass bottom-up participation that traditional approaches can not provide. To address the shortcomings of existing social media data, the new social media platform should be capable of providing particular data on short-term happiness related to the built environment and long-term visions of urban planning from citizens living in that city.

Thus, author proposes a new social media platform that can collect geo-location, expectation, daily comments and feelings of the built environment of individuals, which can provide precise data for proposed quantitative methodology.

To collect such data, a series of qualitative questions will be asked whenever users want to leave a comment on the social media platform. In addition, a notification will pop up when people visit a urban facility or a park, requiring a simple rating of feeling. What is more, throughout the new urban planning process, the government can announce a routine of public events in which it invites public participation and citizen long-term visions for developing their ideal city. A social media platform is proposed as following:

Conclusion

This research shows certain statistic significant relationship between public sentiment and the proximity to amenities, which proves author’s hypothesis that the built environment might affect individual feeling. It shows a potential to help planner understand which characteristics of the built environment may improve residents’ happiness. Importantly, this research establishes a framework to quantify urban experience from text-based information and build a simulation model based on that. Even though the results demonstrate flaws in the use of existing social media data in Jiaxing, they show a significant opportunity to utilize social media platforms to invite mass public participation. Imagine a new social platform platform that allows all citizen to post comments and feelings in the city. The more precise and targeted data could be used in this framework and help build a bottom-up participation in city management and planning.As such, we could see the social media platform as a new form of infrastructure of urban life, which might bring a great potential to design a more people-oriented city or a new urban form.

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